The_Assessor_Summer 2026 Web - Flipbook - Page 10
JOURNAL
IAEA: AI
AI IN VEHICLE DAMAGE
INSPECTION
The critical role of continuous learning from the expert,
by David Punter FInstAEA MFIEA.
rti昀椀cial intelligence
(AI) has moved
beyond being a future
aspiration for the
automotive claims and
repair sector. Today, AI-powered
damage assessment tools are helping
insurers, repairers, 昀氀eet operators,
and accident management providers
process claims faster, improve
operational ef昀椀ciency, and deliver a
more streamlined customer experience.
A
Using advanced computer vision and
machine learning algorithms, these
systems can analyse photographs of
damaged vehicles, identify affected
panels and components, estimate
repair costs, and support triage
decisions within seconds. For an
industry under constant pressure to
reduce claim cycle times and manage
rising repair costs, the bene昀椀ts are
compelling.
The case for AI is strengthened by
the increasing complexity of modern
vehicles. As manufacturers introduce
advanced driver assistance systems
(ADAS), electric vehicle architectures,
lightweight materials, and increasingly
sophisticated electronics, repair
planning has become signi昀椀cantly more
challenging. McKinsey highlights that
while claim frequencies may reduce
over time, the cost and complexity of
repairs continue to rise because of the
growing technological content within
vehicles. [mckinsey.com].
Yet amid the enthusiasm surrounding
AI adoption, an important reality is
often overlooked: machine learning
systems do not possess permanent
expertise.
Unlike conventional software, machine
learning models are entirely dependent
upon the quality and relevance of
the data on which they are trained.
Their performance is not static. In fact,
accuracy can deteriorate over time
if systems are not regularly updated
with new information and validated
outcomes.
10
This phenomenon, commonly known
as model drift, represents one of the
most signi昀椀cant challenges facing
the long-term deployment of AI
within vehicle damage assessment.
IBM de昀椀nes model drift as the
degradation of machine learning
performance resulting from changes
in data, operating conditions, or
the relationship between inputs and
expected outcomes. Models trained
using historical information can
become progressively less effective
as real-world conditions evolve. [ibm.
com].
For the automotive repair sector, the
implications are considerable.
Vehicle construction techniques,
repair methodologies, manufacturer
procedures, labour requirements,
and parts costs are continually
changing. The repair approach for a
contemporary electric vehicle differs
substantially from that of an internal
combustion vehicle built only a few
years ago. New battery protection
systems, structural adhesives,
composite materials, and sensor
technologies introduce variables that
may not exist within older datasets.
If AI models are not continually
retrained using veri昀椀ed repair
outcomes, they risk making decisions
based on outdated assumptions.
This can lead to inaccurate repair
estimates, increased supplements,
misidenti昀椀cation of damage severity,
or inconsistent repair versus totalloss decisions. In short, the model’s
understanding of the vehicle repair
environment gradually diverges from
reality. [ibm.com], [datacamp.com].
This is why human expertise remains
fundamental to the success of AI-driven
inspection processes.
Experienced vehicle damage assessors,
repair engineers, manufacturerapproved technicians, and engineering
specialists provide the validated
outcomes that machine learning
systems require to remain effective.
Their decisions create the feedback
loop through which AI continues to
learn. Every repaired vehicle, every
adjusted estimate, and every con昀椀rmed
repair methodology represents a
valuable data point that can improve
future model performance.
Indeed, the most successful AI
deployments are not designed
to replace technical expertise but
to amplify it. Technology excels
at processing large volumes of
information quickly and consistently.
Industry professionals contribute
context, judgement, accountability,
and an understanding of emerging
repair practices that no algorithm can
independently develop.
As the industry continues its digital
transformation, the conversation
should shift from whether AI can
replace vehicle assessors to how
AI and technical experts can work
together most effectively. The future of
vehicle damage inspection lies not in
autonomous decision-making, but in
a collaborative model where machine
intelligence is continuously informed
by quali昀椀ed human expertise.
Ultimately, AI is only as valuable as its
ability to re昀氀ect current repair realities.
Without ongoing learning from
credible, quali昀椀ed sources, such as the
IAEA and its professional members,
even the most sophisticated damage
assessment platform will gradually
lose relevance. Continuous retraining,
informed by real-world outcomes and
professional oversight, will therefore
be the de昀椀ning factor that separates
successful AI implementations from
those that fail to keep pace with an
evolving automotive landscape.
JOURNAL
References: McKinsey & Company, The Power and
Potential of AI in Insurance Claims and Aviva: Rewiring the
Insurance Claims Journey with AI. [mckinsey.com]. • IBM,
What is Model Drift? and AI model monitoring guidance.
[ibm.com]. • DataCamp, Understanding Data Drift and
Model Drift. [datacamp.com]. • Institute of Automotive
Engineer Assessors, Membership, [iaea-online.org].
THE ASSESSORS JOURNAL | SUMMER 2026 | www.iaea-online.org/news/the-assessor
